Backtest Results For A Simple "Buy the Dip" Strategy

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Today, we will dive into “Backtest Results For A Simple "Buy the Dip" Strategy”

Backtest Results For A Simple “Buy the Dip” Strategy

I came across this trading strategy quite a while ago, and decided to revisit it and do some backtesting, with impressive results, so I wanted to share it and see if there’s anything I missed or any improvements that can be made to it.

If You Missed

Concept:

Strategy concept is quite simple: If the day’s close is near the bottom of the range, the next day is more likely to be an upwards move.

Setup steps are:

Step 1: Calculate the current day’s range (Range = High — Low)

Step 2: Calculate the “close distance”, i.e. distance between the close and the low (Dist = Close — Low)

Step 3: Convert the “close distance” from step 2 into a percentage ([Dist / Range] * 100)

This close distance percentage number tells you how near the close is to the bottom of the day’s range.

Analysis:

To verify the concept, I ran a test in python on 20 years worth of S&P 500 data. I tested a range of distances between the close and the low and measured the probability of the next day being an upwards move.

This is the result. The x axis is the close distance percentage from 5 to 100%. The y axis is the win rate. The horizontal orange line is the benchmark “buy and hold strategy” and the light blue line is the strategy line.

What this shows is that as the “close distance percentage” decreases, the win rate increases.

Backtest:

I then took this further into an actual backtest, using the same 20 years of S&P500 data. To keep the backtest simple, I defined a threshold of 20% that the “close distance” has to be below. If it is, then that’s a signal to go long so I buy at the close of that day and exit at the close of the next day. I also backtested a buy and hold strategy to compare against and these are the results:

The results are quite positive. Not only does the strategy beat buy and hold, it also comes out with a lower drawdown, protecting the capital better. It is also only in the market 19% of the time, so the money is available the rest of the time to be used on other strategies.

Overfitting

There is always a risk of overfitting with this kind of backtest, so one additional step I took was to apply this same backtest across a few other indices. In total I ran this on the S&P, Dow Jones, Nasdaq composite, Russel and Nikkei. The results below show the comparison between the buy and hold (Blue) and the strategy (yellow), showing that the strategy outperformed in every test.

Caveats

While the results look promising, there are a few things to consider.

1. Trading fees/commission/slippage not accounted for and likely to impact results

2. Entries and exits are on the close. Realistically the trades would need to be entered a few minutes before the close, which may not always be possible and may affect the results

Final thoughts

This definitely seems to have potential so it’s a strategy that I would be keen to test on live data with a demo account for a few months. This will give a much better idea of the performance and whether there is indeed an edge.

Does anyone have experience with a strategy like this or with buying dips in general?

More Info

This post is long enough as it is, so for a more detailed explanation I have linked the code and a video below:

Code is here on GitHub: https://github.com/russs123/Buy-The-Dip/tree/main

Video explaining the strategy, code and backtest here: https://youtu.be/rhjf6PCtSWw